The evolution of digital wireless communications has resulted in an array of communication devices that can wirelessly communicate both voice and data information. Among such products are handsets that are being marketed as today as personal communication systems. Such digital handsets may be used not merely for traditional telephony or two-way radio voice communications but also as radio frequency (RF) modems to wirelessly transmit or receive data communications.
One RF modem-type application of these handsets, or other digital wireless communication device, is to connect a handset or device to a data port of a personal computer and to use the handset or device to transmit data from the computer or to receive data transmissions that are broadcast to the computer. An example of such a use could be a utility company employee with a laptop computer transmitting information back to a central office from a remote location in the field and in turn receiving data or other communications from that central office. He could also wirelessly correspond with others and obtain whatever informational assistance he might need without ever having to leave the location at which he is working. In such an application, the wireless modem receives data from the personal computer through its interface with the computer. The modem then modulates that data into an RF signal. Once modulated, the data is then wirelessly transmitted by the modem. The wireless modem is also capable of receiving an RF signal that has been broadcast wirelessly from another data source, demodulating that RF signal, recreating the data that had been sent by the originating data source, and then transmitting that data to the personal computer to which that modem is connected.
A problem presented by such an application is wideband near-field electromagnetic interference that is generated by a personal computer. This interference can cause unacceptable degradation of the RF signal quality if a wireless RF modem is being used. One method of combating this problem is to use two-antenna diversity. Two-antenna diversity uses two antennas to receive a signal and then applies an optimization technique to improve the quality of the received signal over the performance that would be afforded by the use of a single antenna.
One of the simplest forms of two-antenna diversity is two-antenna selection diversity. As its name implies, this method involves selecting one of two antennas as the antenna that will be utilized as the receptor for a particular communication. There are several methods of making that selection. One involves choosing the antenna that has the highest received power. A major drawback to such a system is that it fails to discriminate between signal and interference power. To overcome that problem, J. C. Chang and N. R. Sollenberger proposed in "Burst Coherent Demodulation with Combined Symbol Timing, Frequency Offset Estimation, and Diversity Selection," IEEE Transactions on Communications, Vol. 39, No. 7, July 1991, using a system that indirectly analyzes the clarity, or average opening, of the eye pattern in a QPSK (Quadrature Phase Shift Keying) digital modulation scheme. QPSK is one of many modulation schemes by which digital information is impressed upon an RF carrier by modulation of the carrier's amplitude, frequency, and/or phase. One can represent these modulation schemes as combinations of particular individual points, or constellations, in a complex two-dimensional plane, each point, or symbol, representing one or more data bits and each point being defined by its amplitude and phase location in the complex plane. When an RF carrier is demodulated and sampled at its optimum sampling time, each sample should yield an amplitude and phase measurement that maps to one of the points predesignated by that modulation scheme. Interfering signals and noise can introduce variations in these amplitude and phase measurements, moving the sampled point away from the predesignated points and inserting ambiguity into the determination of which symbol was intended. The eye pattern is a complex plane representation of these measured samples, and the clarity of the eye pattern is an indication of the degree of precision by which the sampled points can be mapped to the predesignated symbols.
In general terms, Chang and Sollenberger propose choosing the antenna that produced the clearest eye pattern. However, they had to come up with a system to estimate its clarity since it would be impractical to actually observe the eye pattern. Their system searches for the optimal sampling point of the signal from an antenna by taking K (k=1, . . . , K) samples of each of N (n=1, . . . , N) symbols in a data burst (a string of symbols). These samples are then split into an in-phase component (I(n,k)) and a quadrature component (Q(n,k)) and mapped into vectors in the complex (I,Q) plane. The vectors from the kth sample of each of the N symbols are then added together to create K vector sums (of N vectors each). The ideal sampling point for that signal is then determined to be the sampling point that results in the maximum vector sum magnitude. This sampling point gives, on average, the highest "signal-to-impairment ratio (maximum eye-opening)" and the magnitude of its vector sum is a good measure of the quality of the signal from that antenna, according to Chang and Sollenberger.
Further improvement in the performance of the diversity system can be obtained by combining the signals from the two antennas rather than just selecting one or the other as the receptor. Amplitude and phase adjustments may be made to the signal from each antenna, or element, before the signals are combined. An adaptive antenna array is a diversity system that can make these adjustments and change its pattern in response to changes in the signal environment, seeking to optimize signal quality at the array output through a system of feedback control. Such systems are discussed in Adaptive Antennas, Concepts and Performance, by R. T. Compton, Jr., published by Prentice-Hall, Englewood Cliffs, N.J., 1988. A block diagram of a typical adaptive two-antenna array is shown in FIG. 1. The adaptive antenna array includes two antennas, 102 and 104, a summer 110, a signal quality measurement block 112, a weighter 114, and mixers 106 and 108 which are located between each of the antennas 102 and 104 and their respective inputs to the summer 110. In these systems, complex weights (amplitude and phase) set by the weighter 114 are applied in the mixers 106 and 108 to the signals from both antennas 102 and 104 before these signals are combined in the summer 110. After combining there is some measure of the quality of the received signal, and via a system of feedback control these weights are readjusted until the quality measurements are optimized. One method of weight optimization discussed by Compton is based on a minimum mean square error concept. A block diagram illustration of a quality measurement block 112 utilizing this concept is shown in FIG. 2. The receiver has a reference signal 204 to which it compares a correlated array output signal 202. An error signal 208 is generated and the weighter 114 adjusts the weights to minimize this error signal 208. Compton also discusses adjusting the weights to maximize the ratio of desired signal power to undesired interference plus noise power (SINR) at the output of the array but has no proposal of how to go about measuring this. See also "Signal Acquisition and Tracking with Adaptive Arrays in the Digital Mobile Radio System IS-54 with Flat Fading," authored by J. H. Winters, in the November 1993 issue of IEEE Transactions on Vehicular Technology, Vol. 42, No. 4, pp. 377-384 (Winters proposes using the sequence of symbols in a frame that are sent for synchronization purposes as the desired signal, comparing that sequence to a reference signal consisting of the known synchronization sequence in the receiver, and then using a minimum mean square error algorithm to determine the appropriate complex weights).
One known benefit of these adaptive arrays is that their gain patterns can be modified through the adjustment of weights. An N element array, by adjusting the weight accorded to the signal received at each element, can create N-1 nulls in its gain pattern and effectively cancel out N-1 interfering signals. With two elements, a system of proper weighting can steer a null toward a single interference source, such as a personal computer directly or indirectly connected to the antenna array.
The above described adaptive antenna array systems all require a reference signal in the receiver, which is then compared to a correlated signal that is transmitted with the data. This reduces system efficiency in that it consumes space in the transmitted frame that might otherwise be used for the transmission of information. An improvement on this system is proposed in a paper entitled "Low-complexity Diversity Combining Algorithms and Circuit Architectures for Co-channel Interference Cancellation and Frequency-Selective Fading Mitigation," authored by P. B. Wong and D. C. Cox, published by the Center for Telecommunications, Stanford University, Stanford, Calif., and dated Feb. 6, 1996, a copy of which is attached hereto and which is incorporated herein by reference.
Wong and Cox dispense with the need for training sequences and reference signals by applying a search algorithm that maximizes the average opening of the eye pattern similar to that proposed by Chang and Sollenberger. In general terms, Wong and Cox propose applying complex weights to the signal from just one of the two branches in a two antenna array. The signal quality of the array output is then computed using the averaging opening of the eye pattern methodology proposed by Chang and Sollenbeger. The weighting amplitude and phase is then varied in "coarse" steps, i.e., the amplitude of the signal from that branch is varied by factors of 1/2, 1, and 2 and the phase is varied from 0 degrees to 270 degrees in 90 degree increments. For each combination of amplitude and phase, the average opening of the eye pattern is recomputed. The combination that yields the largest average eye opening is then selected and a "fine" search around that point is undertaken, i.e., the amplitude and phase are varied from that point in increments half the size of the preceding increments. Again, based on the average opening of the eye pattern, an optimal weight is selected. This is then the weight that is used. The problem is that this system requires extra circuitry in the receiver and/or imposes a significant computational and memory load on the receiver since the weights are calculated downstream in the receiver process after the RF signal has been demodulated into the transmitted symbols. Furthermore, utilization of an average eye opening methodology is problematic in digital systems with large or unknown apriori constellations (e.g., systems that will use one of two or more QAM modulation schemes depending upon the situation). During the coarse search the eye might be open only over a very narrow range of complex weights, or perhaps might never yield a significant opening. So there is a need for a way to adjust the weights that is more robust than analyzing the eye openings and which is independent from constellation type. Also, this is a system that is exclusively applicable to digital transmissions in that eye patterns are not available in an analog environment.